1. Field of the Invention
This invention relates generally to spectroscopic data processing technology and its application in calibration and noninvasive measurement of blood and/or tissue constituent properties, such as glucose concentration. More particularly, a method and apparatus are disclosed that use near-infrared light to detect and quantify physiological and chemical properties of an irradiated tissue sample. Still more particularly, this invention relates to a series of steps for processing noninvasive spectra using extraction of net analyte signal and/or interference removal to correct near-infrared spectra that are obscured, distorted, and/or corrupted as a result of sample heterogeneity, the dynamic nature of skin, and the chemical composition of the sampled tissue.
2. Background Discussion of the Prior Art
Diabetes
Diabetes is a leading cause of death and disability worldwide that afflicts an estimated 16 million Americans. Complications of diabetes include heart and kidney disease, blindness, nerve damage, and high blood pressure with the estimated total cost to the United States economy alone exceeding $90 billion per year, (Diabetes Statistics, National Institutes of Health, Publication No. 98-3926, Bethesda, Md. (November 1997); JAMA, vol. 290, pp. 1884-1890 (2003)). Long-term clinical studies show that the onset of complications are significantly reduced through proper control of blood glucose concentrations, (The Diabetes Control and Complications Trial Research Group, The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus, N. Eng. J. of Med., vol. 329, pp. 977-86 (1993); U.K. Prospective Diabetes Study (UKPDS) Group, Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes, Lancet, vol. 352, pp. 837-853 (1998); and Y. Ohkubo, H. Kishikawa, E. Araki, T. Miyata, S. Isami, S. Motoyoshi, Y. Kojima, N. Furuyoshi, M. Shichizi, Intensive insulin therapy prevents the progression of diabetic microvascular complications in Japanese patients with non-insulin-dependent diabetes mellitus: a randomized prospective 6-year study, Diabetes Res. Clin. Pract., vol. 28, pp. 103-117 (1995)).
A vital element of diabetes management is the self-monitoring of blood glucose concentrations by diabetics in the home environment. Unfortunately, current monitoring techniques discourage regular use due to the inconvenient and painful nature of drawing blood through the skin prior to analysis. Therefore, new methods for self-monitoring of blood glucose concentrations are required to improve the prospects for more rigorous control of blood glucose concentration in diabetic patients.
Numerous approaches have been explored for measuring blood glucose concentrations ranging from invasive methods, such as microdialysis, to noninvasive technologies that rely on spectroscopy. Each method has associated advantages and disadvantages, but only a few have received approval from certifying agencies. Unfortunately, noninvasive techniques for the self-monitoring of blood glucose have not yet been certified.
Noninvasive Glucose Concentration Estimation
One method, near-infrared spectroscopy involves the illumination of a spot on the body with near-infrared electromagnetic radiation, such as light in the wavelength range about 750 to 2500 nm. The light is partially absorbed and scattered, according to its interaction with the constituents of the tissue prior to being reflected back to a detector. The detected light contains quantitative information that is based on the known interaction of the incident light with components of the body tissue, such as water, fat, protein, and glucose.
Previously reported methods for the noninvasive measurement of glucose concentration through near-infrared spectroscopy rely on the detection of the magnitude of light attenuation caused by the absorption signature of blood glucose, as represented in the targeted tissue volume. The tissue volume is the portion of irradiated tissue from which light is reflected or transmitted to the spectrometer detection system. The signal due to the absorption of glucose is extracted from the spectral measurement through various methods of signal processing and one or more mathematical models. The models are developed through the process of calibration on the basis of an exemplary calibration set of spectral measurements and associated reference blood glucose concentrations, such as fingertip, venous, or alternative site blood.
There are a number of reports on noninvasive glucose concentration estimation technologies. Some of these relate to general instrumentation configurations required for noninvasive glucose concentration determination, while others refer to sampling and processing technologies. Those related to the invention are briefly reviewed here:
Near-infrared spectroscopy has been demonstrated in specific studies to represent a feasible and promising approach to the noninvasive prediction of blood glucose concentrations. M. Robinson, R. Eaton, D. Haaland, G. Keep, E. Thomas, B. Stalled, P. Robinson, Noninvasive glucose monitoring in diabetic patients: A preliminary evaluation, Clin. Chem., vol. 38. pp. 1618-22 (1992) describe three different instrument configurations for measuring diffuse transmittance through the finger in the 600 to 1300 nm range. Meal tolerance tests were used to perturb the glucose concentrations of three subjects and calibration models were constructed specific to each subject on single days and tested through cross-validation. Absolute average prediction errors ranged from 19.8 to 37.8 mg/dL.
H. Heise, R. Marbach, T. Koschinsky, F. Gries, Noninvasive blood glucose sensors based on near-infrared spectroscopy, Artif. Org. vol. 18. pp. 439-47 (1994) and R. Marbach, T. Koschinsky, F. Gries, H. Heise Noninvasive glucose assay by near-infrared diffuse reflectance spectroscopy of the human inner lip, Appl. Spect. vol. 47, pp. 875-81 (1992) describe a diffuse reflectance measurement of the oral mucosa in the 1111 to 1835 nm range with an optimized diffuse reflectance accessory. In-vivo experiments were conducted on single diabetic subjects using glucose tolerance tests and on a population of 133 different subjects. The best standard error of prediction reported was 43 mg/dL, which was obtained from a two-day single person oral glucose tolerance test that was evaluated through cross-validation.
K. Jagemann, C. Fischbacker, K. Danzer K, U. Muller, B. Mertes Application of near-infrared spectroscopy for noninvasive determination of blood/tissue glucose using neural network, Z. Phys. Chem., vol. 191S, pp. 179-190 (1995); C. Fischbacher, K. Jagemann, K. Danzer, U. Muller, L. Papenkrodt, J. Schuler Enhancing calibration models for noninvasive near-infrared spectroscopic blood glucose determinations, Fresenius J. Anal. Chem., vol. 359, pp.78-82 (1997); K. Danzer, C. Fischbacher, K. Jagemann Near-infrared diffuse reflection spectroscopy for noninvasive blood-glucose monitoring, LEOS Newsletter, vol. 12(2), pp. 9-11 (1998); and U. Muller, B. Mertes, C. Fischbacher, K. Jagemann, K. Danzer Noninvasive blood glucose monitoring by means of new infrared spectroscopic methods for improving the reliability of the calibration models, Int. J. Artif. Organs, vol. 20, pp. 285-290 (1997) describe diffuse reflectance spectra over the 800 to 1350 nm range on the middle finger of the right hand with a fiber-optic probe. Each experiment involved a diabetic subject and was conducted over a single day with perturbation of blood glucose concentrations through carbohydrate loading. Results, using both partial least squares regression and radial basis function neural networks were evaluated on single subjects over single days through cross-validation. Danzer, supra, reports an average root mean square prediction error of 36 mg/dL through cross-validation over 31 glucose profiles.
J. Burmeister, M. Arnold, G. Small Human noninvasive measurement of glucose using near infrared spectroscopy [abstract], Pittsburgh Conference, New Orleans, La. (1998) describe absorbance spectra, collected in transmission mode, of the tongue in the 1429 to 2000 nm range. A study of five diabetic subjects was conducted over a 39-day period with five samples taken per day. Every fifth sample was used for an independent test set and the standard error of prediction for all subjects was greater than 54 mg/dL.
T. Blank, T. Ruchti, S. Malin, S. Monfre, The use of near-infrared diffuse reflectance for the noninvasive prediction of blood glucose, IEEE Lasers and electro-optics society newsletter, vol. 13, no. 5 (October 1999) describe the noninvasive measurement of blood glucose concentration during modified oral glucose tolerance tests over a short time period. The calibration was customized for an individual and tested over a relatively short time period.
In all of these studies, limitations were cited that affect the acceptance of such a method as a commercial product. These limitations included sensitivity, sampling problems, time lag, calibration bias, long-term reproducibility, and instrument noise. Fundamentally, however, accurate noninvasive estimation of blood glucose concentration is presently limited by the available near-infrared technology, the trace concentration of glucose relative to other constituents, and the dynamic nature of the skin and living tissue of the patient (O. Khalil Spectroscopic and clinical aspects of noninvasive glucose measurements, Clin. Chem., vol. 45, pp. 165-77 (1999)).
S. Malin, T. Ruchti, An intelligent system for noninvasive blood analyte prediction, U.S. Pat. No. 6,280,381 (Aug. 28, 2001) describe chemical, structural, and physiological variations that produce dramatic and nonlinear changes in the optical properties of the tissue sample. The measurement is further complicated by the heterogeneity of the sample, the multi-layered structure of the skin, rapid variation related to hydration levels, changes in the volume fraction of blood in the tissue, hormonal stimulation, temperature fluctuations, and variation of blood constituent concentrations. These issues are further considered through a discussion of the scattering properties of skin.
Tissue Scattering Properties
Skin Structure
The structure and composition of skin varies widely among individuals. In addition, skin properties vary at different sites and over time on the same individual at the same site. The outer layer of skin comprises a thin layer known as the stratum corneum, a stratified cellular epidermis, and an underlying dermis of connective tissue. Below the dermis is the subcutaneous fatty layer or adipose tissue. The epidermis is the thin outer layer that provides a barrier to infection and loss of moisture, while the dermis is the thick inner layer that provides mechanical strength and elasticity. The epidermis layer is 10 to 150 μm thick and is divided into three layers, the basal, middle, and superficial layers. The basal layer borders the dermis and contains pigment-forming melanocyte cells, keratinocyte cells, langherhan cells, and merkel cells. In humans, the thickness of the dermis ranges from 0.5 mm over the eyelid to 4 mm on the back and averages approximately 1.2 mm over most of the body.
In the dermis, water accounts for approximately seventy percent of the volume. The next most abundant constituent is collagen, a fibrous protein comprising 70 to 75 percent of the dry weight of the dermis. Elastin fibers, also a protein, are plentiful though they constitute only a small proportion of the bulk. In addition, the dermis contains a wide variety of structures, such as sweat glands, hair follicles, blood vessels, and other cellular constituents. Conversely, the subcutaneous layer, adipose tissue, is by volume approximately ten percent water and includes primarily cells rich in triglycerides and/or fat. The concentration of glucose varies in each layer according to the water content, the relative sizes of the fluid compartments, the distribution of capillaries, and the perfusion of blood. Due to the high concentration of fat and fats tendency to repel water and glucose that water carries, the average concentration of glucose in subcutaneous tissue is significantly lower compared to the glucose concentration in the dermis.
Optical Properties of Skin
When near-infrared light is delivered to the skin, a percentage of it is reflected while the remainder penetrates into the skin. The proportion of reflected light, specular reflectance, is typically between four to seven percent of the delivered light over the entire spectrum from 250 to 3000 nm, for a perpendicular angle of incidence. The 93 to 96 percent of the incident light that enters the skin is attenuated due to absorption or scattering within the many layers of the skin. These two processes taken together essentially determine the penetration of light into skin, the tissue volume that is sampled by the light, and the transmitted or remitted light that is scattered from the skin.
Diffuse reflectance or remittance is defined as that fraction of incident optical radiation that is returned from a turbid sample. Alternately, diffuse transmittance is the fraction of incident optical radiation which is transmitted through a turbid sample. Absorption by various skin constituents account for the spectral extinction of the light within each layer. Scattering is the process by which photons are redirected to the skin surface to contribute to the observed diffuse reflectance of the skin.
Scattering in tissue is due to discontinuities in the refractive index on the microscopic level, such as the aqueous-lipid membrane interfaces between each tissue compartment or the collagen fibrils within the extracellular matrix.
The spatial distribution and intensity of scattered light depends upon the size and shape of the particles relative to the wavelength and upon the difference in refractive index between the medium and the constituent particles. The scattering of the dermis is dominated by the scattering from collagen fiber bundles in the 2.8 μm diameter range occupying 21 percent of the dermal volume and the refractive index mismatch is 1.38/1.35. The spectral characteristics of diffuse remittance from tissue are the result of a complex interplay of the intrinsic absorption and scattering properties of the tissue, the distribution of the heterogeneous scattering components, and the geometry of the points of irradiation relative to the points of light detection.
Absorbance of light in tissue is primarily due to three fundamental constituents: water, protein, and fat. As the main constituent, water dominates the near-infrared absorbance above 1100 nm and is observed through pronounced absorbance bands. Protein in its various forms, and in particular collagen, is a strong absorber of light that irradiates the dermis. Near-infrared light that penetrates to subcutaneous tissue is absorbed primarily by fat. In the absence of scattering, the absorbance of near-infrared light due to a particular analyte, A, is approximated by Beers Law at each wavelength according to:A=εbC  (1)where ε is the analyte specific absorption coefficient, C is the concentration, and b is the pathlength. The overall absorbance at a particular wavelength is the sum of the individual absorbances of each particular analyte given by Beer's Law. The concentration of a particular analyte, such as glucose, can be determined through multivariate analysis of the absorbance over a multiplicity of wavelengths because ε is unique for each analyte. However, in tissue compartments expected to contain glucose, the concentration of glucose is at least three orders of magnitude lower than that of water. Consequently, the signal targeted for detection by reported approaches to near-infrared measurement of glucose concentration, the absorbance due to glucose in the tissue, is expected to be at most three orders of magnitude less than other interfering tissue constituents. Therefore, the near-infrared measurement of glucose concentration requires a high level of sensitivity over a broad wavelength range and the application of methods of multivariate analysis.
The diverse scattering characteristics of the skin cause light returning from an irradiated sample to vary in a highly nonlinear manner with respect to tissue analytes and in particular glucose. Simple linear models, such as Beer's Law, are invalid for analysis of highly scattering matrices, such as the dermis. This is a recognized problem and several reports have disclosed unique methods for compensating for the nonlinearity of the measurement while providing the necessary sensitivity [E. Thomas, R. Rowe, Methods and Apparatus for Tailoring Spectroscopic Calibration Models, U.S. Pat. No. 6,157,041 (Dec. 5, 2000)].
Dynamic Properties of Skin
While knowledge and use of the optical properties of the skin, high instrument sensitivity, and compensation for the inherent nonlinearities are vital for the application of near-infrared spectroscopy to noninvasive blood analyte measurement, an understanding of biological and chemical mechanisms that lead to time dependent changes in the optical properties of skin tissue is equally important and yet largely ignored. At a given measurement site, skin tissue is often assumed to be static except for changes in the target analyte and other absorbing species. However, variations in the physiological state of tissue profoundly effect the optical properties of tissue layers and compartments over a relatively short period of time. Such variations, are often dominated by fluid compartment equalization through water shifts and are related to hydration levels and changes in blood analyte levels.
Total body water accounts for over sixty percent of the weight of the average person and is distributed among two major compartments: the extracellular fluid, which comprises one-third of total body water, and the intracellular fluid, which comprises two-thirds of total body water. The extracellular fluid, in turn, is divided into the interstitial fluid (extravascular) and the blood plasma (intravascular). Water permeable lipid membranes separate the compartments and water is transferred rapidly between them through diffusion to equalize the concentrations of water and other analytes across the membrane. Water flux from one compartment to another is driven by osmosis and the amount of pressure required to prevent osmosis is called the osmotic pressure. Under static physiological conditions the fluid compartments are at equilibrium. However, during a net fluid gain or loss as a result of water intake or loss, all compartments gain or lose water proportionally.
Distribution of substances contained in blood serum that are needed by the tissues, such as water and glucose, occurs through the process of diffusion. The movement of water and other analytes from intravascular to extravascular compartments occurs rapidly as tremendous numbers of water and other molecules, in constant thermal motion, diffuse back and forth through the capillary wall. On average, the rate at which water molecules diffuse through the capillary membrane is about 80 times greater than the rate at which the plasma itself flows linearly along the capillary. The actual diffusion rate is proportional to the concentration difference between the two compartments and the permeability of the molecule. Water, for example, is approximately 1.7 times more permeable than glucose.
Short-term changes in blood glucose concentration lead to a corresponding change in blood osmolality. Fluid is rapidly re-distributed accordingly and results in a change in the water concentration of each body compartment. For example, the osmotic effect of hyperglycemia is a movement of extravascular, i.e. cell and interstitial fluid, water to the intravascular space. Conversely, a decrease in blood glucose concentration leads to a movement of water to extravascular space from the intravascular compartment.
Because the cell membrane is relatively impermeable to most solutes but highly permeable to water, whenever there is a higher concentration of a solute on one side of the cell membrane, water diffuses across the membrane toward the region of higher solute concentration. Large osmotic pressures can develop across the cell membrane with relatively small changes in the concentration of solutes in the extracellular fluid. As a result, relatively small changes in concentration of impermeable solutes in the extracellular fluid, such as glucose, can cause tremendous changes in cell volume. These changes in cell volume are observed in noninvasive near-infrared spectra of tissue.
Several methods are reported to compensate in some part for the dynamic variation of the tissue. For example, K. Hazen, Glucose determination in biological matrices using near-infrared spectroscopy, Doctoral Dissertation, University of Iowa, (August 1995) and J. Burmeister In-vitro model for human noninvasive blood glucose measurements, Doctoral Dissertation, University of Iowa (December 1997) describe several methods of noninvasive glucose measurement that use calibration models that are specific to an individual over a short period of time. This approach avoids modeling the differences between patients and therefore does not generalize to more individuals. However, the calibration models have not been tested over long time periods, do not provide means for compensating for the varying optical properties of the sample, and do not address variation related to the dynamic water shifts of fluid compartments.
Malin, supra, reports a method for compensating for variation related to the structure and state of the tissue through an intelligent pattern recognition system capable of determining calibration models that are most appropriate for the patient at the time of measurement. The calibration models are developed from the spectral absorbance of a representative population of patients that have been segregated into groups. The groups or classes are defined on the basis of structural and state similarity such that the variation within a class is small compared to the variation between classes. Classification occurs through extracted features of the tissue absorbance spectrum related to the current patient state and structure. However, the invention does not use features for directly compensating for physiological changes in the tissue. Further, the direct use of features representing the physiological state of the subject's measurement site for noninvasive measurement of glucose was not described.
Thomas, supra, identifies a method for reducing intra-subject variation through the process of mean-centering both the direct and indirect measurements. However, this does not address the key problem related to sample heterogeneity and complexity, physiological and chemical variation related to the dynamic nature of the tissue, and the common problem of optical variation which occurs from sample-to-sample.
Several approaches exist that employ diverse preprocessing methods to remove spectral variation related to the sample and instrument variation including multiplicative signal correction (P. Geladi, D. McDougall, H. Martens Linearization and scatter-correction for near-infrared reflectance spectra of meat, Appl. Spect., vol. 39, pp. 491-500 (1985)), standard normal variate transformation (R. J. Barnes, M. Dhanoa, S. Lister, Appl. Spect., vol. 43, pp. 772-777 (1989)), piecewise multiplicative scatter correction (T. Isaksson, B. Kowalski, Appl. Spect., 47, pp. 702-709 (1993)), extended multiplicative signal correction (H. Martens, E. Stark, J. Pharm Biomed Anal, vol. 9, pp. 625-635, (1991)), pathlength correction with chemical modeling and optimized scaling (T. Isaksson, Z. Wang, B. Kowalski, J. Near Infrared Spect., vol. 1, pp. 85-97 (1993)), and finite impulse response (FIR) filtering, (S. Sum, Spectral signal correction for multivariate calibration, Doctoral Dissertation, University of Delaware, (Summer 1998); S. Sum, S. Brown, Standardization of fiber-optic probes for near-infrared multivariate calibrations, Appl. Spect., vol. 52, no. 6, pp.869-877 (1998); and T. Blank, S. Sum, S. Brown, S. Monfre Transfer of near-infrared multivariate calibrations without standards, Analytical Chemistry, vol. 68, pp. 2987-2995 (1996)).
Sum (summer 1998), supra, further describes a practical solution to variation due to changes in a given physical sample and instrument effects through the use of signal preprocessing techniques. The reported methods reduce the variance in the spectral measurement arising from non-chemical sources while retaining the variance caused by chemical change. The sources of variance include the physical traits of the sample, such as, particle size and shape, packing density, heterogeneity, and surface roughness. The methods include preprocessing through a derivative step followed by a spectral transformation through either multiplicative scatter correction or standard normal variate transformation.
In addition, a diversity of signal, data or pre-processing techniques are commonly reported with the fundamental goal of enhancing accessibility of the net analyte signal. The net analyte signal refers to the portion of the spectral signal related to the target analyte that is orthogonal to the interference (A. Lorber, K. Faber and B. Kowalski, Net analyte signal calculation in multivariate calibration, Anal. Chem, vol. 69, pp. 1620-1626, (1997); A. Oppenheim, R. Schafer, Digital signal processing, Englewood Cliffs, N.J.: Prentice Hall, pp. 195-271 (1975); and A. Savitzky, M. Golay Smoothing and differentiation of data by simplified least squares procedures, Anal. Chem., vol. 36, no. 8, pp.1627-1639 (1964)).
Problem Statement
While known methods for preprocessing effectively compensate for variation related to instrument and physical changes in the sample and enhance the net analyte signal in the presence of noise and interference, they are as reported inadequate for compensating for the sources of tissue related variation defined above. For example, highly nonlinear effects related to sampling different tissue locations cannot be effectively compensated for through a pathlength correction because the sample is multi-layered and heterogeneous. In addition, fundamental assumptions inherent in these methods, such as the constancy of multiplicative and additive effects across the spectral range and homoscadasticity of noise are violated in the noninvasive tissue application. In particular, re-distribution of water between various tissue compartments alter the optical properties of the tissue through changes in the water concentration, the concentration of other analytes, the refractive indices of various layers, the thickness of tissue layers, and the size and distribution of scattering centers. Therefore, the optical properties of the tissue sample are modified in a highly nonlinear and profound manner. In addition, the actual tissue volume sampled and the effective or average pathlength of light is varied. No method for preprocessing a near-infrared spectral measurement is reported that effectively compensates for the complex, heterogeneous, layered, and dynamic composition of tissue; the profound variation over time, from sample-to-sample and between patients; and the changes in optical properties related to the re-distribution of water between various tissue compartments.